This paper tests portfolio efficiency in a multivariate context using Bayesian techniques and extends the Bayesian portfolio efficiency literature by using Highest Posterior Density (HPD) regions as an inferential tool. Inference is conducted using return-based and utility-based measures. An...

We study the effect of parameter uncertainty on the long-run risk for three asset classes: stocks, bills and bonds. We model return dynamics as a Bayesian vector autoregression. With an uninformative prior we find that parameter uncertainty raises the annualised long-run volatilities of all...

Designing an investment strategy in transition economies is a difficult task because stock-markets opened through time, time series are short, and there is little guidance how to obtain expected returns and covariance matrices necessary for mean-variance portfolio allocation. Also, structural...

Are excess returns predictable and if so, what does this mean for investors? Previous literature has tended toward two polar viewpoints: that predictability is useful only if the statistical evidence for it is incontrovertible, or that predictability should affect portfolio choice, even if the...

We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model is calibrated at a quarterly frequency for ten European countries. We also use maximum-likelihood estimates and Bayesian estimates to account for parameter uncertainty. We find...

We apply Bayesian methods to study a common VAR-based approach for decomposing the variance of excess stock returns into components reflecting news about future excess stock returns, future real interest rates, and future dividends. We develop a new prior elicitation strategy which involves...

This paper develops a Bayesian approach to estimating exchange rate target zone models and rational expectations models in general. It also introduces a simultaneous-equation target zone model that incorporates stochastic realignment risk. Using FF/DM and IL/DM exchange rate data, we find that...

Stock markets employ specialized traders, market-makers, designed to provide liquidity and volume to the market by constantly supplying both supply and demand. In this paper, we demonstrate a novel method for modeling the market as a dynamic system and a reinforcement learning algorithm that...

Recent theoretical models imply that liquidity is fragile: financial markets are liquid in some equilibria and illiquid in others. This paper employs an intuitively appealing Markov-switching regime model to investigate the episodic nature of stock market illiquidity and the intertemporal...

Recent developments in nonlinear time series modelling are reviewed. Three main types of nonlinear models are discussed: Markov Switching, Threshold Autoregression and Smooth Transition Autoregression. Classical and Bayesian estimation techniques are described for each model. Parametric tests...